Smart ring system for monitoring uvb exposure levels and using machine learning technique to predict high risk driving behavior

ABSTRACT

The described systems and methods determine a driver&#39;s fitness to safely operate a moving vehicle based at least in part upon observed UVB exposure patterns, where the driver&#39;s UVB exposure levels may serve as a proxy for vitamin D levels in that driver&#39;s body. A smart ring, wearable on a user&#39;s finger, continuously monitors user&#39;s exposure to UVB light. This UVB exposure data, representing UVB exposure patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user&#39;s level of risk exposure based at least in part upon observed UVB exposure patterns. The user can be warned of this risk to prevent them from driving or to encourage them to get more sunlight exposure before driving. In some instances, the disclosed smart ring system may interact with the user&#39;s vehicle to prevent it from starting while exposed to high risk due to deteriorated psychological or physiological conditions stemming from insufficient UVB exposure.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/877,391, filed Jul. 23, 2019, incorporated by reference hereinfor all purposes.

FIELD OF DISCLOSURE

The present disclosure generally relates to implementations of smartring wearable devices and, more particularly, to utilizing a smart ringfor predicting a driver's fitness to safely operate a moving vehiclebased at least in part upon the measured driver exposure to UVB light.

BACKGROUND

Driving for prolonged periods of time, especially when performed onconsistent basis, can be taxing on the body. It is important for driversto monitor their health as much as possible and when able, preventdisease. Among many factors that can affect drivers' health andperformance, the levels of vitamin D can be often overlooked.

The active form of vitamin D is a hormone, not a vitamin. Hereinafter,we will refer to both the active and the precursor forms of vitamin D asa vitamin, using their common names. Vitamin D receptors are expressedin many tissues, including skin, bone, muscle, brain, endocrine tissues,and the immune system. This indicates that the body relies on vitamin Dfor proper functioning. The major source of vitamin D isexogenous—synthesized in the skin, when ultraviolet B (UVB) energyphotolyzes a cholesterol precursor (7-dehydrocholesterol) to vitamin D₃.The synthesized vitamin is carried to the liver, and further to thekidneys to be converted into the biologically active form of vitamin D,calcitriol, that engages with the tissues.

The typical concerns with UV radiation (UVR) focus on excessiveexposure, which can result in sunburn and skin cancer caused byexcessive radiation. However, excessive UVR exposure accounts for asmall fraction of the total global burden of disease. In contrast, amarkedly larger annual disease burden results from very low levels ofUVR exposure. This burden includes major disorders of themusculoskeletal system and possibly an increased risk of variousautoimmune diseases and life-threatening cancers.

Vitamin D deficiency or insufficiency has also been associated withmental wellbeing, and even linked with anxiety disorders. In turn, highlevels of stress and anxiety can directly and indirectly impact drivers'ability to stay focused on the task of driving. Heightened emotions maycreate a cognitive distraction that can impede drivers' capacity tonotice and respond to hazards, and undermined health may offer furtherdiversion of attention away from the road. All of these factors can leadto risky driving behavior and compromise safety on the roads for thedrivers and those around them.

BRIEF SUMMARY

The present disclosure relates to a smart wearable ring system andmethods that allow for continuous monitoring of the user's levels of UVBexposure and using that data to determine a ring wearer's fitness tosafely operate a moving vehicle.

The described systems and techniques address the challenge forindividuals to identify that they may be in an impaired state, andfurther to assess how their own psychological or physiologicalconditions may impact their driving ability. More specifically, thedisclosed smart ring collects UVB exposure data representing UVBexposure patterns for a particular user. This data can be utilized, incombination with driving data, as training data for a machine learning(ML) model to train the ML model to predict high risk driving based atleast in part upon observed UVB exposure. A user can be warned of thisrisk to prevent them from driving or to encourage them to delay drivingand take a suggested remediating action. In some instances, thedisclosed smart ring system may interact with the user's vehicle toprevent it from starting while the user is exposed to high risk due todeteriorated psychological or physiological conditions stemming frominadequate UVB exposure.

The amount of bioactive vitamin D generated from skin exposure tosunlight (incident UVB radiation) depends on environmental and personalfactors, such as the individual's skin tone, age, weight, bodytemperature, gut health, the health of liver and kidneys, as well as thelatitude, time of day, clothing type (how penetrable is the clothing toUVB wavelength, and surface area coverage), and pollution factors.Utilizing a machine learning model to correlate the user's UVB exposurepatterns with driving behavior patterns allows for a highly personalizedprediction of driving behavior without quantifying all the steps leadingto the production of vitamin D or quantifying the vitamin D levels.

The conventional method for determining vitamin D levels is a bloodtest, performed in a laboratory. There is currently no conventionalmethod for non-invasive monitoring of the daily levels of exogenousvitamin D production and, moreover, no known method for correlating UVBexposure levels that lead to vitamin D production with driving behavior.

In an embodiment, an inconspicuous and comfortable ring-shaped device,intended to be worn on a user's hand, is outfitted with sensors that areable to measure the user's UVB exposure. A system trains and implementsa Machine Learning (ML) algorithm to make a personalized prediction ofthe level of driving risk exposure based at least in part upon thecaptured UVB exposure data. The ML model training may be achieved, forexample, at a server by first (i) acquiring, via a smart ring, one ormore sets of first data indicative of one or more UVB exposure patterns;(ii) acquiring, via a driving monitor device, one or more sets of seconddata indicative of one or more driving patterns; (iii) utilizing the oneor more sets of first data and the one or more sets of second data astraining data for a ML model to train the ML model to discover one ormore relationships between the one or more UVB exposure patterns and theone or more driving patterns, wherein the one or more relationshipsinclude a relationship representing a correlation between a given UVBexposure pattern and a high-risk driving pattern.

In an embodiment, the trained ML model analyzes a particular set of datacollected by a particular smart ring associated with a user, and (i)determines that the particular set of data represents a particular UVBexposure pattern corresponding to the given UVB exposure patterncorrelated with the high-risk driving pattern; and (ii) responds to saiddetermining by predicting a level of risk exposure for the user duringdriving.

The method may further include: (i) predicting a level of driving riskexposure to a driver based at least in part upon analyzed UVB exposurepatterns; and (ii) communicating the predicted risk exposure; and (iii)determining remediating action to reduce or eliminate the driving risk;or communicate or implement the remediating action in accordance withvarious embodiments disclosed herein.

Generally speaking, the described determinations regarding remediationmay be made prior to the ring user attempting driving, thereby enablingthe smart ring and any associated systems to prevent or discourage theuser from driving while exposed to high risk due to a deterioratedpsychological or physiological conditions stemming inadequate exposureto UVB light.

UVB radiation does not penetrate glass. Therefore, if a driver spendsthe daylight hours inside a vehicle behind glass windows, such driverwill not receive adequate UVB exposure, and will not generate arecommended vitamin D amount. The additional advantage of the disclosedsystem is in encouraging drivers with deficient exposure to UVB light totake a break from driving and spend time outside in sunlight, which isoften accompanied with overall health-inducing movement and exercise.Skin and eyes exposure to sunlight has been associated with triggeringand releasing hormones, such as serotonin, which is a major player inelevating mood, helping to feel calm and focused. Monitoring andmaintaining healthy levels of UVB exposure may be an incentive fordrivers to increase outdoor activity and improve health andpsychological well-being—all very important factors for enhancing driverability to safely operate a vehicle.

Depending upon the embodiment, one or more benefits may be achieved.These benefits and various additional objects, features and advantagesof the present disclosure can be fully appreciated with reference to thedetailed description and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system comprising a smart ring and a block diagramof smart ring components.

FIG. 2 illustrates a number of different form factor types of a smartring.

FIG. 3 illustrates examples of different smart ring surface elements.

FIG. 4 illustrates example environments for smart ring operation.

FIG. 5 illustrates example displays.

FIG. 6 shows an example method for training and utilizing a ML modelthat may be implemented via the example system shown in FIG. 4 .

FIG. 7 illustrates example methods for assessing and communicatingpredicted level of driving risk exposure.

FIG. 8 shows example vehicle control elements and vehicle monitorcomponents.

FIG. 9 illustrates a flow diagram of a method for estimating the amountof vitamin D produced in the user's skin and communicatingrecommendation to a user accordingly.

DETAILED DESCRIPTION

FIG. 1 , FIG. 2 , FIG. 3 , FIG. 3 , FIG. 4 , FIG. 5 , FIG. 6 , FIG. 7 ,FIG. 8 , and FIG. 9 discuss various techniques, systems, and methods forimplementing a smart ring to train and implement a machine learningmodule capable of predicting a driver's risk exposure based at least inpart upon observed UVB exposure patterns. Notably, a person's UVBexposure over a given period of time may serve as a proxy for vitamin Dlevels in that person's body. Because vitamin D deficiency may result incognitive impairment (which result in poor, high-risk vehicleoperation), it may be desirable to measure a person's vitamin D levels.Where this direct measurement is not feasible, it may be desirable totrack a person's UVB exposure as a proxy. Further, this tracked data maybe fed to a machine-learning model along with corresponding drivingpattern data to observe relationships between specific UVB exposurelevels and certain driving patterns. For example, UVB exposure levelsbelow a threshold may result in high(er)-risk driving patterns (whichmay be due to the fact that the person is vitamin D deficient, a factorwhich may be correlated with the UVB exposure). As a result, UVBexposure levels may be tracked and used, along with a machine-learningmodel, to predict poor driving performance and to take corrective orpreventative action, thus improving driver safety.

Below, sections I-III and V describe, with reference to FIG. 1 , FIG. 2, FIG. 3 , and FIG. 5 , example smart ring systems, form factor types,and components. Section IV describes, with reference to FIG. 4 , anexample smart ring environment. Sections VI and VII describe, withreference to FIG. 6 and FIG. 7 , example methods that may be implementedvia the smart ring systems described herein. And Section VIII describes,with reference to FIG. 8 , example elements of a vehicle that maycommunicate with one of the described smart ring systems to facilitateimplementation of the functions described herein.

I. Example Smart Ring and Smart Ring Components

FIG. 1 illustrates a smart ring system 100 for predicting a level ofdriving risk exposure to a driver based at least in part upon one ormore analyzed UVB exposure patterns, comprising (i) a smart ring 101including a set of components 102 and (ii) one or more devices orsystems that may be electrically, mechanically, or communicativelyconnected to the smart ring 101, according to an embodiment.Specifically, the system 100 may include any one or more of: a charger103 for the smart ring 101, a user device 104, a network 105, a mobiledevice 106, a vehicle 108, or a server 107. The charger 103 may provideenergy to the smart ring 101 by way of a direct electrical, a wireless,or an optical connection. The smart ring 101 may be in a directcommunicative connection with the user device 104, the mobile device106, the server 107, or a vehicle 108 by way of the network 105.Interactions between the smart ring 101 and other components of thesystem 100 are discussed in more detail in the context of FIG. 4 .

The smart ring 101 may sense a variety of signals indicative of:activities of a user wearing the ring 101, measurements of physiologicalparameters of the user, or aspects of the user's environment. The smartring 101 may analyze the sensed signals using built-in computingcapabilities or in cooperation with other computing devices (e.g., userdevice 104, mobile device 106, server 107, or vehicle 108) and providefeedback to the user or about the user via the smart ring 101 or otherdevices (e.g., user device 104, mobile device 106, server 107, orvehicle 108). Additionally or alternatively, the smart ring 101 mayprovide the user with notifications sent by other devices, enable secureaccess to locations or information, or a variety of other applicationspertaining to health, wellness, productivity, or entertainment. Itshould be understood that while some figures and select embodimentdescriptions refer to a vehicle in the form of an automobile, thetechnology is not limited to communicating with automotive vehicles.That is, references to a “vehicle” may be understood as referring to anyhuman-operated transportation device or system, such as a train,aircraft, watercraft, submersible, spacecraft, cargo truck, recreationalvehicle, agricultural machinery, powered industrial truck, bicycle,motorcycle, hovercraft, etc.

The smart ring 101, which may be referred to herein as the ring 101, maycomprise a variety of mechanical, electrical, electrochemical, optical,electro-optical, or any other suitable subsystems, devices, components,or parts disposed within, at, throughout, or in mechanical connection toa housing 110 (which may be ring shaped and generally configured to beworn on a finger). Additionally, a set of interface components 112 a and112 b may be disposed at the housing, and, in particular, through thesurface of the housing. The interface components 112 a and 112 b mayprovide a physical access (e.g., electrical, fluidic, mechanical, oroptical) to the components disposed within the housing. The interfacecomponents 112 a and 112 b may exemplify surface elements disposed atthe housing. As discussed below, some of the surface elements of thehousing may also be parts of the smart ring components.

As shown in FIG. 1 , the components 102 of the smart ring 101 may bedistributed within, throughout, or on the housing 110. As discussed inthe contexts of FIG. 2 and FIG. 3 below, the housing 110 may beconfigured in a variety of ways and include multiple parts. The smartring components 102, for example, may be distributed among the differentparts of the housing 110, as described below, and may include surfaceelements of the housing 110. The housing 110 may include mechanical,electrical, electrochemical, optical, electro-optical, or any othersuitable subsystems, devices, components, or parts disposed within or inmechanical connection to the housing 110, including a battery 120, acharging unit 130, a controller 140, a sensor system 150 comprising oneor more sensors, a communications unit 160, a one or more user inputdevices 170, or a one or more output devices 190. Each of the components120, 130, 140, 150, 160, 170, and/or 190 may include one or moreassociated circuits, as well as packaging elements. The components 120,130, 140, 150, 160, 170, and/or 190 may be electrically orcommunicatively connected with each other (e.g., via one or more bussesor links, power lines, etc.), and may cooperate to enable “smart”functionality described within this disclosure.

The battery 120 may supply energy or power to the controller 140, thesensors 150, the communications unit 160, the user input devices 170, orthe output devices 190. In some scenarios or implementations, thebattery 120 may supply energy or power to the charging unit 130. Thecharging unit 130, may supply energy or power to the battery 120. Insome implementations, the charging unit 130 may supply (e.g., from thecharger 103, or harvested from other sources) energy or power to thecontroller 140, the sensors 150, the communications unit 160, the userinput devices 170, or the output devices 190. In a charging mode ofoperation of the smart ring 101, the average power supplied by thecharging unit 130 to the battery 120 may exceed the average powersupplied by the battery 120 to the charging unit 130, resulting in a nettransfer of energy from the charging unit 130 to the battery 120. In anon-charging mode of operation, the charging unit 130 may, on average,draw energy from the battery 120.

The battery 120 may include one or more cells that convert chemical,thermal, nuclear or another suitable form of energy into electricalenergy to power other components or subsystems 140, 150, 160, 170,and/or 190 of the smart ring 101. The battery 120 may include one ormore alkaline, lithium, lithium-ion and or other suitable cells. Thebattery 120 may include two terminals that, in operation, maintain asubstantially fixed voltage of 1.5, 3, 4.5, 6, 9, 12 V or any othersuitable terminal voltage between them. When fully charged, the battery120 may be capable of delivering to power-sinking components an amountof charge, referred to herein as “full charge,” without recharging. Thefull charge of the battery may be 1, 2, 5, 10, 20, 50, 100, 200, 500,1000, 2000, 5000, 10000, 20000 mAh or any other suitable charge that canbe delivered to one or more power-consuming loads as electrical current.

The battery 120 may include a charge-storage device, such as, forexample a capacitor or a super-capacitor. In some implementationsdiscussed below, the battery 120 may be entirely composed of one or morecapacitive or charge-storage elements. The charge storage device may becapable of delivering higher currents than the energy-conversion cellsincluded in the battery 120. Furthermore, the charge storage device maymaintain voltage available to the components or subsystems 130, 140,150, 160, 170, and/or 190 when one or more cells of the battery 120 areremoved to be subsequently replaced by other cells.

The charging unit 130 may be configured to replenish the charge suppliedby the battery 120 to power-sinking components or subsystems (e.g., oneor more of subsystems 130, 140, 150, 160, 170, and/or 190) or, morespecifically, by their associated circuits. To replenish the batterycharge, the charging unit 130 may convert one form of electrical energyinto another form of electrical energy. More specifically, the chargingunit 130 may convert alternating current (AC) to direct current (DC),may perform frequency conversions of current or voltage waveforms, ormay convert energy stored in static electric fields or static magneticfields into direct current. Additionally or alternatively, the chargingunit 130 may harvest energy from radiating or evanescent electromagneticfields (including optical radiation) and convert it into the chargestored in the battery 120. Furthermore, the charging unit 130 mayconvert non-electrical energy into electrical energy. For example, thecharging unit 130 may harvest energy from motion, or from thermalgradients.

The controller 140 may include a processor unit 142 and a memory unit144. The processor unit 142 may include one or more processors, such asa microprocessor (μP), a digital signal processor (DSP), a centralprocessing unit (CPU), a graphical processing unit (GPU), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or any other suitable electronic processing components.Additionally or alternatively, the processor unit 142 may includephotonic processing components.

The memory unit 144 may include one or more computer memory devices orcomponents, such as one or more registers, RAM, ROM, EEPROM, or on-boardflash memory. The memory unit 144 may use magnetic, optical, electronic,spintronic, or any other suitable storage technology. In someimplementations, at least some of the functionality the memory unit 144may be integrated in an ASIC or and FPGA. Furthermore, the memory unit144 may be integrated into the same chip as the processor unit 142 andthe chip, in some implementations, may be an ASIC or an FPGA.

The memory unit 144 may store a smart ring (SR) routine 146 with a setof instructions, that, when executed by the processor 142 may enable theoperation and the functionality described in more detail below.Furthermore, the memory unit 144 may store smart ring (SR) data 148,which may include (i) input data used by one or more of the components102 (e.g., by the controller when implementing the SR routine 146) or(ii) output data generated by one or more of the components 102 (e.g.,the controller 140, the sensor unit 150, the communication unit 160, orthe user input unit 170). In some implementations, other units,components, or devices may generate data (e.g., diagnostic data) forstoring in the memory unit 144.

The processing unit 142 may draw power from the battery 120 (or directlyfrom the charging unit 130) to read from the memory unit 144 and toexecute instructions contained in the smart ring routine 146. Likewise,the memory unit 144 may draw power from the battery 120 (or directlyfrom the charging unit 130) to maintain the stored data or to enablereading or writing data into the memory unit 144. The processor unit142, the memory unit 144, or the controller 140 as a whole may becapable of operating in one or more low-power mode. One such low powermode may maintain the machine state of the controller 140 when less thana threshold power is available from the battery 120 or during a chargingoperation in which one or more battery cells are exchanged.

The controller 140 may receive and process data from the sensors 150,the communications unit 160, or the user input devices 170. Thecontroller 140 may perform computations to generate new data, signals,or information. The controller 140 may send data from the memory unit144 or the generated data to the communication unit 160 or the outputdevices 190. The electrical signals or waveforms generated by thecontroller 140 may include digital or analog signals or waveforms. Thecontroller 140 may include electrical or electronic circuits fordetecting, transforming (e.g., linearly or non-linearly filtering,amplifying, attenuating), or converting (e.g., digital to analog, analogto digital, rectifying, changing frequency) of analog or digitalelectrical signals or waveforms.

In various embodiments, the sensor unit 150 may include one or moresensors disposed within or throughout the housing 110 of the ring 101.Each of the one or more sensors may transduce one or more of: light,sound, acceleration, translational or rotational movement, strain,pressure, temperature, chemical composition, surface conductivity orother suitable signals into electrical or electronic sensors or signals.The one or more sensors may be acoustic, photonic,micro-electro-mechanical systems (MEMS) sensors, chemical,electrochemical, micro-fluidic (e.g., flow sensor), or any othersuitable type of sensor. The sensor unit 150 may include, for example, alight intensity meter, or a radiometer for measuring the intensity andexposure time per wavelength of UV radiation (100 nm-400 nm) on the ring101. The sensor unit 150 may include, for example, one or more ofthree-axis accelerometers for detecting orientation and movement of thering 101. The sensor unit 150 may alternatively or additionally includean inertial measurement unit (IMU) for detecting orientation andmovement of the ring 101, such as one having one or more accelerometersand/or altimeters. The sensor unit 150 may include, for example,electrochemical immunosensors, which may be further integrated withmicrofluidic devices to monitor the levels of cortisol and/or otherhormones which levels can change in response to stress. The sensor unit150 may include, for example, a microphone, or any other suitable devicethat converts sound into an electrical signal. The sensor unit 150 mayalso be equipped with a Global Positioning System (GPS) receiver,providing data indicative, but not limiting to latitude, longitude,elevation, date, and time. The one or more sensors of the sensor unit150 may provide data indicative, but not limiting to, the user's heartrate (HR), blood pressure, body temperature, skin conductance, skinperfusion, the amount of sweat and its composition, sunlight and/or UVradiation exposure, ambient temperature, and vehicular motion data (whenthe ring user is positioned inside of a moving vehicle). The one or moresensors of the sensor unit 150 may additionally provide the user'sbehavioral data, such as data on gesticulation, hand grip pressure, bodymotion data, and enabled with voice and sound processing and speechrecognition.

The communication unit 160 may facilitate wired or wirelesscommunication between the ring 101 and one or more other devices. Thecommunication unit 160 may include, for example, a network adaptor toconnect to a computer network, and, via the network, tonetwork-connected devices. The computer network may be the Internet oranother type of suitable network (e.g., a personal area network (PAN), alocal area network (LAN), a metropolitan area network (MAN), a wide areanetwork (WAN), a mobile, a wired or wireless network, a private network,a virtual private network, etc.). The communication unit 160 may use oneor more wireless protocols, standards, or technologies forcommunication, such as Wi-Fi, near field communication (NFC), Bluetooth,or Bluetooth low energy (BLE). Additionally or alternatively, thecommunication unit 160 may enable free-space optical or acoustic links.In some implementations, the communication unit 160 may include one ormore ports for a wired communication connections. The wired connectionsused by the wireless communication module 160 may include electrical oroptical connections (e.g., fiber-optic, twisted-pair, coaxial cable).

User input unit 170 may collect information from a person wearing thering 101 or another user, capable of interacting with the ring 101. Insome implementations, one or more of the sensors in the sensor unit 150may act as user input devices within the user input unit 170. User inputdevices may transduce tactile, acoustic, video, gesture, or any othersuitable user input into digital or analog electrical signal and sendthese electrical signals to the controller 140.

The output unit 190 may include one or more devices to outputinformation to a user of the ring 101. The one or more output devicesmay include acoustic devices (e.g., speaker, ultrasonic); haptic,thermal, electrical devices; electronic displays for optical output,such as an organic light emitting device (OLED) display, a laser unit, ahigh-power light-emitting device (LED), etc.; or any other suitabletypes of devices. For example, the output unit 190 may include aprojector that projects an image onto a suitable surface. In someimplementations, the sensor unit 150, the user input unit 170, and theoutput unit 190 may cooperate to create a user interface withcapabilities (e.g., a keyboard) of much larger computer systems, asdescribed in more detail below.

The components 120, 130, 140, 150, 160, 170, and/or 190 may beinterconnected by a bus (not shown), which may be implemented using oneor more circuit board traces, wires, or other electrical,optoelectronic, or optical connections. The bus may be a collection ofelectrical power or communicative interconnections. The communicativeinterconnections may be configured to carry signals that conform to anyone or more of a variety of protocols, such as I2C, SPI, or other logicto enable cooperation of the various components.

II. Example Smart Ring Form Factor Types

FIG. 2 includes block diagrams of a number of different example formfactor types or configurations 205 a, 205 b, 205 c, 205 d, 205 e, and/or205 f of a smart ring (e.g., the smart ring 101). The configurations 205a, 205 b, 205 c, 205 d, 205 e, and/or 205 f (which may also be referredto as the smart rings 205 a, 205 b, 205 c, 205 d, 205 e, and/or 205 f)may each represent an implementation of the smart ring 101, and each mayinclude any one or more of the components 102 (or components similar tothe components 102). In some embodiments, one or more of the components102 may not be included in the configurations 205 a, 205 b, 205 c, 205d, 205 e, and/or 205 f. The configurations 205 a, 205 b, 205 c, 205 d,205 e, and/or 205 f include housings 210 a-f, which may be similar tothe housing 110 shown in FIG. 1 .

The configuration 205 a may be referred to as a band-only configurationcomprising a housing 210 a. In the configuration 205 b, a band mayinclude two or more removably connected parts, such as the housing parts210 b and 210 c. The band may also have an inner diameter rangingbetween 13 mm and 23 mm. The two housing parts 210 b and 210 c may eachhouse at least some of the components 102, distributed between thehousing parks 210 b and 210 c in any suitable manner.

The configuration 205 c may be referred to as a band-and-platformconfiguration comprising (i) a housing component 210 d and (ii) ahousing component 210 e (sometimes called the “platform 210 e”), whichmay be in a fixed or removable mechanical connection with the housing210 d. The platform 210 e may function as a mount for a “jewel” or forany other suitable attachment. The housing component 210 d and theplatform 210 e may each house at least one or more of the components 102(or similar components).

In some instances, the term “smart ring” may refer to a partial ringthat houses one or more components (e.g., components 102) that enablethe smart ring functionality described herein. The configurations 205 dand 205 e may be characterized as “partial” smart rings and may beconfigured for attachment to a second ring. The second ring may be aconventional ring without smart functionality, or may be second smartring, wherein some smart functionality of the first or second rings maybe enhanced by the attachment.

The configuration 205 d, for example, may include a housing 210 f with agroove to enable clipping onto a conventional ring. The grooved clip-onhousing 210 f may house the smart ring components described above. Theconfiguration 205 e may clip onto a conventional ring using asubstantially flat clip 210 g part of the housing and contain the smartring components in a platform 210 h part of the housing.

The configuration 205 f, on the other hand, may be configured to becapable of being mounted onto a finger of a user without additionalsupport (e.g., another ring). To that end, the housing 210 i of theconfiguration 205 f may be substantially of a partial annular shapesubtending between 180 and 360 degrees of a full circumference. Whenimplemented as a partial annular shape, the housing 210 i may be moreadaptable to fingers of different sizes that a fully annular band (360degrees) and may be elastic. A restorative force produced by adeformation of the housing 210 i may ensure a suitable physical contactwith the finger. Additional suitable combinations of configurations (notillustrated) may combine at least some of the housing features discussedabove.

III. Example Smart Ring Surface Elements

FIG. 3 includes perspective views of example configurations 305 a, 305b, 305 c, 305 d, 305 e, and/or 305 f of a smart right (e.g., the smartring 101) in which a number of surface elements are included.

Configuration 305 a is an example band configuration 205 a of a smartring (e.g., smart ring 101). Some of the surface elements of the housingmay include interfaces 312 a, 312 b that may be electrically connectedto, for example, the charging unit 130 or the communications unit 160.On the outside of the configuration 305 a, the interfaces 312 a, 312 bmay be electrically or optically connected with a charger to transferenergy from the charger to a battery (e.g., the battery 120), or withanother device to transfer data to or from the ring 305 a. The outersurface of the configuration 305 a may include a display 390 a, whilethe inner surface may include a biometric sensor 350 a.

The configurations 305 b and 305 c are examples of configurations of asmart ring with multiple housing parts (e.g., configuration 205 b inFIG. 2 ). Two (or more) parts may be separate axially (configuration 305b), azimuthally (configuration 305 c), or radially (nested rings, notshown). The parts may be connected mechanically, electrically, oroptically via, for example, interfaces analogous to interfaces 312 a,312 b in configuration 305 a. Each part of a smart ring housing may haveone or more surface elements, such as, for example, sensors 350 b, 350 cor output elements 390 b, 390 c. The latter may be LEDs (e.g., outputelement 390 b) or haptic feedback devices (e.g., output element 390 c),among other suitable sensor or output devices. Additionally oralternatively, at least some of the surface elements (e.g., microphones,touch sensors) may belong to the user input unit 170.

Configuration 305 d may be an example of a band and platformconfiguration (e.g., configuration 205 c), while configurations 305 eand 305 f may be examples of the partial ring configurations 205 d and205 e, respectively. Output devices 390 d, 390 e, 390 f on thecorresponding configurations 305 d, 305 e, 305 f may be LCD display,OLED displays, e-ink displays, one or more LED pixels, speakers, or anyother suitable output devices that may be a part of a suite of outputsrepresented by an output unit (e.g., output unit 190). Other surfaceelements, such as an interface component 312 c may be disposed within,at, or through the housing. It should be appreciated that a variety ofsuitable surface elements may be disposed at the illustratedconfigurations 305 a, 305 b, 305 c, 305 d, 305 e, and/or 305 f atlargely interchangeable locations. For example, the output elements 390d, 390 e, 390 f may be replaced with sensors (e.g., UV sensor, ambientlight or noise sensors, etc.), user input devices (e.g., buttons,microphones, etc.), interfaces (e.g., including patch antennas oroptoelectronic components communicatively connected to communicationsunits), or other suitable surface elements.

IV. Example Environments for Smart Ring Operation

FIG. 4 illustrates an example environment 400 within which a smart ring405 may be configured to operate. In an embodiment, the smart ring 405may be the smart ring 101. In some embodiments, the smart ring 405 maybe any suitable smart ring capable of providing at least some of thefunctionality described herein. Depending on the embodiment, the smartring 405 may be configured in a manner similar or equivalent to any ofthe configurations 205 a, 205 b, 205 c, 205 d, 205 e, and/or 205 f or305 a, 305 b, 305 c, 305 d, 305 e, and/or 305 f shown in FIG. 2 and FIG.3 .

The smart ring 405 may interact (e.g., by sensing, sending data,receiving data, receiving energy) with a variety of devices, such asbracelet 420 or another suitable wearable device, a mobile device 422(e.g., a smart phone, a tablet, etc.) that may be, for example, the userdevice 104, another ring 424 (e.g., another smart ring, a charger forthe smart ring 405, etc.), or a steering wheel 438 (or another vehicleinterface). Additionally or alternatively, the smart ring 405 may becommunicatively connected to a network 440 (e.g., Wifi, 5G cellular),and by way of the network 440 (e.g., network 105 in FIG. 1 ) to a server450 (e.g., server 107 in FIG. 1 ), a personal computer 444 (e.g., mobiledevice 106), or a vehicle 446 (which may be the vehicle 108).Additionally or alternatively, the ring 405 may be configured to senseor harvest energy from natural environment, such as the sun 450.

A. Example of Server 450

The server 450 is an electronic computing device including at least onenon-transitory computer-readable memory 452 storing instructionsexecutable on a processor unit 454, and a communication unit 456, eachof which may be communicatively connected to a system bus (not shown) ofthe server 450. In some instances, the described functionality of theserver 450 may be provided by a plurality of servers similar to theserver 450. The memory 452 of the server 450 includes a Machine Learning(ML) model training module 452 a, and a ML model module 452 b, which area set of machine-readable instructions (e.g., a software module,application, or routine). In some embodiments, the server 450 canfunction as a database to store data utilized by the ML modules 452 aand 452 b, as well as the model results.

At a high level, the ML model 452 b is configured to predict the user'slevel of driving risk exposure based at least in part upon the user'sUVB exposure data, and the ML training module 452 a is configured totrain the model module 452 b with the user's UVB exposure data incombination with driving data. The Machine Learning model modules 452 aand 452 b are described in greater detail in FIG. 6 below.

B. Example of Ring Communicating with Other Devices

The ring 405 may exchange data with other devices by communicativelyconnecting to the other devices using, for example, the communicationunit 160. The communicative connection to other device may be scheduled,initiated by the ring 405 in response to user input via the user inputunit 170, in response to detecting trigger conditions using the sensorunit 150, or may be initiated by the other devices. The communicativeconnection may be wireless, wired electrical connection, or optical. Insome implementation, establishing a communicative link may includeestablishing a mechanical connection.

The ring 405 may connect to other devices (e.g., a device with thebuilt-in charger 103) to charge the battery 120. The connection to otherdevices for charging may enable the ring 405 to be recharged without theneed for removing the ring 405 from the finger. For example, thebracelet 420 may include an energy source that may transfer the energyfrom the energy source to battery 120 of the ring 405 via the chargingunit 130. To that end, an electrical (or optical) cable may extend fromthe bracelet 420 to an interface (e.g., interfaces 112 a, 112 b, 312 a,312 b) disposed at the housing (e.g., housings 110, 210 a, 210 b, 210 c,210 d, 210 e, 210 f, 210 g, 210 h, and/or 210 i) of the ring 405. Themobile device 422, the ring 424, the steering wheel 438 may also includeenergy source configured as chargers (e.g., the charger 103) for thering 405. The chargers may transfer energy to the ring 405 via a wiredor wireless (e.g., inductive coupling) connection with the charging unit130 of the ring 405.

V. Example Displays

FIG. 5 illustrates a set of example display devices 500 according tovarious embodiments, including example displays 500 a, 500 b, 500 c, 500d, 500 e, 500 f that may be provided by way of a smart ring such as thesmart ring 101 of FIG. 1 or 405 of FIG. 5 , for the purpose ofdisplaying information relevant to monitored UVB exposure patterns,predicted risk exposure, and a remediating action to restore oreliminate risk exposure (e.g., providing a user notification). Each ofthe display devices 500 may be part of the system 100 shown in FIG. 1 ,and each may be utilized in place of or in addition to any one or moreof the display devices shown in FIG. 1 . Each display device 500 may besimilar in nature to any of the display devices of ring 405, user device422, mobile device 444, or vehicle 446 shown in FIG. 4 , capable ofperforming similar functions and interfacing with the same or similarsystems; and each of the devices 101, 405, 422, 444, and 446 may provideoutput via any of the displays 500 a, 500 b, 500 c, 500 d, 500 e, 500 f,in addition to or in place of their respective displays, if desired.

In an embodiment, the display devices 500 may display the level ofdriving risk exposure data (e.g., as a score, a figure, a graph, asymbol, or a color field, etc.), the estimated amount of vitamin Dgenerated in the user's skin (e.g., as a written text, a number, ascore, a figure, or a symbol, etc.), comparison of the estimated amountof vitamin D generated in the user's skin to the recommended dailyamount of vitamin D (e.g., as a written text, a number, a score, afigure, or a symbol, etc.), and the suggested remediating actions (e.g.,as a written text, a code, a figure, a graph, or a symbol, etc.).Examples of remediating actions will be described later in more detail.More generally, each of the display devices 500 may present visualinformation based at least in part upon data received from any of thedevices 405, 422, 444, 446, or the server 450 shown in FIG. 4 .

As shown, the display device 500 a is a screen of a mobile phone 522(e.g., representing an example of the mobile device 422) that may becoupled to the smart ring 405. The display device 500 b is an in-dashdisplay of a vehicle 546 (e.g., representing an example of a displayintegrated into the dash or console of the vehicle 446) that may becoupled to the smart ring 405. The display device 500 c is a projectorfor smart ring 505 (e.g., representing an example of the smart ring405), which could be part of the ring output unit 190 and its exampleoutput devices 390 d, 390 e, 390 f. The display device 500 d is aheads-up display (HUD) for a vehicle (e.g., the vehicle 446) projectedonto a windshield 517, which may also communicate with the smart ring405 via the network 440. Alert 518 is a sample alert, which may displayto the user any combination of a predicted level of driving risk exposer(e.g., driving risk score) and a suggested remediating action. Thedisplay device 500 e is a screen for a tablet 544 (e.g., representing anexample of the mobile device 444, which may communicate with the smartring 405). The display device 500 f is a screen for a laptop 521 (e.g.,representing an example of the mobile device 444, which may communicatewith the smart ring 405) that may be coupled to the smart ring 405.

VI. An Example Method of Developing and Utilizing a Machine LearningModel

FIG. 6 depicts an example method 600 for training, evaluating andutilizing the Machine Learning (ML) model for predicting the level ofdriving risk exposure based at least in part upon acquired sensor dataindicative of one or more UVB exposure patterns. At a high level, themethod 600 includes a step 602 for model design and preparation, a step604 for model training and evaluation, and a step 606 for modeldeployment.

Depending on the implementation, the ML model may implement supervisedlearning, unsupervised learning, or semi-supervised learning. Supervisedlearning is a learning process for generalizing on problems where aprediction is needed. A “teaching process” compares predictions by themodel to known answers (labeled data) and makes corrections in themodel. In such an embodiment, the driving data may be labeled accordingto a risk level (e.g., depending on the nature and severity of swerving,braking, observed driver distraction, proximity to other vehicles, ratesof acceleration, etc.). Unsupervised learning is a learning process forgeneralizing the underlying structure or distribution in unlabeled data.In an embodiment utilizing unsupervised learning, the system may rely onunlabeled UVB exposure data, unlabeled driving data, or some combinationthereof. During unsupervised learning, natural structures are identifiedand exploited for relating instances to each other. Semi-supervisedlearning can use a mixture of supervised and unsupervised techniques.This learning process discovers and learns the structure in the inputvariables, where typically some of the input data is labeled, and mostis unlabeled. The training operations discussed herein may rely on anyone or more of supervised, unsupervised, or semi-supervised learningwith regard to the UVB exposure data and driving data, depending on theembodiment.

A. Example of Machine Learning Model Preparation

The step 602 may include any one or more steps or sub-steps 624, 626,628, which may be implemented in any suitable order. At the step 624,the ML model training module 452 a receives from the processor unit 454via the communication unit 456, one or more first training data setsindicative of one or more UVB exposure patterns for training theselected model.

In some embodiments, the one or more sets of the first training data maybe collected from any suitable UVB exposure monitoring device, forexample the smart ring 405 (equipped with the one or more ring sensors150), the user device 444 (e.g., a dedicated user UVB exposuremonitoring device), the mobile device 422 equipped with the ability tocollect and transmit a variety of data indicative of user UVB exposurepatterns (e.g., a smart phone), or an external database (not shown). Inone embodiment, the training data may contain the captured UVB radiationintensity and exposure time at the one or more light intensity sensorswith a sensitivity in the 280-315 nm region, correlated with the data onthe user's body temperature, and the data on the user's estimatedexposed surface area and clothing permeability to UV rays, estimatedfrom the date, time, latitude, ambient temperature, and elevation of theuser.

In some embodiments, in addition to the UVB exposure patterns, the firsttraining data sets may include data indicative of the user's stresslevel patterns. In some embodiments, the one or more additional sets ofthe first training data may be collected from any suitable stressmonitoring device, for example the smart ring 405 (equipped with the oneor more ring sensors 150), the user device 444 (e.g., a dedicated stressmonitoring device), the mobile device 422 equipped with the ability tocollect and transmit a variety of data indicative of stress patterns(e.g., a smart phone), a built-in device of the vehicle 446 (e.g., adevice capable of observing, collecting, and transmitting driver'sstress indicators), or an external database (not shown). In oneembodiment, the training stress data may contain the user'sphysiological data acquired from the one or more physiological sensors,the data on the user's level of stress hormone or hormones acquired fromthe one or more electrochemical sensors, the data on the user'sgesticulation from the one or more motion sensors, the data on theuser's hand grip pressure acquired from the one or more pressuresensors, and audio data from the one or more microphones. These data,for example, may contain measurements of the user's heart rate, bloodpressure, body temperature, skin conductance, sweat amount and sweatconcentration of particular substances, blood levels of particularsubstances, hand movements and gestures indicative of a person understress, the data on sounds and utterances from the user indicative of aperson under stress, and a date and time stamp of these measurements.

In some embodiments, the first training data sets may include dataindicative of UVB exposure patterns for users other than the userassociated with the smart ring, in addition to or instead of dataindicative of UVB exposure patterns for the user associated with thesmart ring. In such embodiment, the population first training data setsas well as the captured first training data sets may include data on theindividuals' skin tone, age, and weight, in order to more accuratelyaccount for the individual factors influencing UVB light absorption. Theadditional data may be acquired from one or more suitable data sourcesdescribed above (the ring 405, or the user device 422, or the mobiledevice 444).

The first training data sets may be stored on the server memory 452, orthe ring memory unit 144, or any other suitable device or itscomponent(s).

At the step 626, the ML module 452 a receives from the processor unit454 via the communication unit 456, one or more second training datasets indicative of one or more driving patterns for training the machinelearning model. This second training data may be collected from the ring405, a vehicle computer 810 of the vehicle 446, the user device 422(e.g., a mobile phone), the mobile device 444 (e.g., a laptop), or anyother suitable electronic driving tracker configured for trackingdriving patterns, or an external database (not shown) that has receivedthe second training data from any suitable means. The data may containtracking of the behavior of the vehicle 446, while operated by the userwearing the ring 405 (e.g., braking, accelerating/decelerating,swerving, proximity to other vehicles, adherence to lane markers andother road markers, adherence to speed limits, etc.).

In some embodiments, the second training data sets may include dataindicative of driving patterns for users other than the user associatedwith the smart ring in addition to or instead of data indicative ofdriving patterns for the user associated with the smart ring.

At the step 628, the ML module receives test data for testing the modelor validation data for validating the model (e.g., from one of thedescribed respective data sources). Some or all of the training, test,or validation data sets may be labeled with a pre-determined scale ofdriving risk scores and thresholds indicative of trigger conditions. Thedeveloped model may utilize this scale to rank the target features ofthe model, and in some implementations determine the level of drivingrisk exposure.

B. Example of Machine Learning Model Training

The ML model development and evaluation module of the step 604, whichtakes place in the ML model training module 452 a, may include any oneor more steps or sub-steps 642, 644, 646, which may be implemented inany suitable order. In a typical example, at step 642, the trainingmodule 452 a trains the ML model 452 b by running the one or moretraining data sets described above. At step 644, the module 452 aevaluates the model 452 b, and at step 646, the module 452 a determineswhether or not the model 452 b is ready for deployment before eitherproceeding to step 606 or returning to step 642 to further develop,test, or validate the model.

Regarding the sub-step 642 of the step 604, developing the modeltypically involves training the model using training data. At a highlevel, machine-learning models are often utilized to discoverrelationships between various observable features (e.g., betweenpredictor features and target features) in a training dataset, which canthen be applied to an input dataset to predict unknown values for one ormore of these features given the known values for the remainingfeatures. These relationships are discovered by feeding the modeltraining data including instances each having one or more predictorfeature values and one or more target feature values. The model then“learns” an algorithm capable of calculating or predicting the targetfeature values (e.g., high risk driving patterns) given the predictorfeature values (e.g., UVB exposure patterns).

Regarding the sub-step 644 of the step 604, evaluating the modeltypically involves testing the model using testing data or validatingthe model using validation data. Testing/validation data typicallyincludes both predictor feature values and target feature values (e.g.,including UVB exposure patterns for which corresponding driving patternsare known), enabling comparison of target feature values predicted bythe model to the actual target feature values, enabling one to evaluatethe performance of the model. This testing/validation process isvaluable because the model, when implemented, will generate targetfeature values for future input data that may not be easily checked orvalidated. Thus, it is advantageous to check one or more accuracymetrics of the model on data for which you already know the targetanswer (e.g., testing data or validation data), and use this assessmentas a proxy for predictive accuracy on future data. Example accuracymetrics include key performance indicators, comparisons betweenhistorical trends and predictions of results, cross-validation withsubject matter experts, comparisons between predicted results and actualresults, etc.

Regarding the sub-step 646 of the step 604, the processor unit 454 mayutilize any suitable set of metrics to determine whether or not toproceed to the step 606 for model deployment. Generally speaking, thedecision to proceed to the step 606 or to return to the step 642 willdepend on one or more accuracy metrics generated during evaluation (thestep 644). After the sub-steps 642, 644, 646 of the step 604 have beencompleted, the processor unit 454 may implement the step 606.

C. Example of Machine Learning Model Implementation

The step 606 may include any one or more steps or sub-steps 662, 664,666, 668, which may be implemented in any suitable order. In a typicalexample, the processor unit 454 collects input data (step 662), loadsthe input data into the model module 452 b (step 664), runs the modelwith the input data (step 666), and stores results generated fromrunning the model on the memory 452 (step 668).

Note, the method 600 may be implemented in any desired order and may beat least partially iterative. That is, the step 602 may be implementedafter the step 604 or after the step 606 (e.g., to collect new data fortraining, testing, or validation), and the step 604 may be implementedafter the step 606 (e.g., to further improve the model via training orother development after deployment).

VII. Example Methods for Assessing and Communicating Predicted Level ofDriving Risk Exposure

FIG. 7 illustrates a flow diagram for an exemplary method 700 forimplementing the ML model module 452 b to: (i) predict a level ofdriving risk exposure to a driver (e.g., by determining the driving riskscore) based at least in part upon analyzed UVB exposure patterns; (ii)communicate the predicted risk exposure (e.g., generate a notificationto alert the user of the predicted level of risk exposure); and (iii)determine remediating action to reduce or eliminate the driving risk; orcommunicate or implement the remediating action in accordance withvarious embodiments disclosed herein. Generally speaking, the describeddeterminations regarding remediation may be made prior to the ring userattempting driving, thereby enabling the smart ring and any associatedsystems to prevent or discourage the user from driving while exposed tohigh risk due to a deteriorated psychological or physiologicalconditions stemming from inadequate UVB exposure.

The method 700 may be implemented by way of all, or part, of thecomputing devices, features, and/or other functionality describedregarding FIG. 1 , FIG. 4 , FIG. 5 , FIG. 6 . At a high level, theserver 450 receives UVB exposure data and predicts a level of drivingrisk exposure (e.g., represented by a risk score) based at least in partupon the UVB exposure data. In an embodiment, the predicted level ofrisk exposure may be a binary parameter having two possible values(e.g., high and low risk), a ternary parameter having three possiblevalues (e.g., high, medium, low), or a parameter having any suitablenumber of values (e.g., a score-based parameter having a value of 0-10,0-100, etc.). Then, based at least in part upon the predicted riskexposure, a remediation may be determined and implemented (e.g., by thesystem 100) or communicated to the user (e.g., via one of the exampledisplay devices 500 of the system 100, or other suitable devices of thering output unit 190) to prevent or dissuade driving while a highexposure to risk exists.

More specifically, in an embodiment, the ML model module 452 b of server450 receives one or more particular UVB exposure data sets from one ormore data sources (step 702). In some embodiments, this data may becollected from one or more smart ring sensors 105, the user device 422(e.g., a smart phone), or the mobile device 444 (e.g., a UVB exposuremeasuring device). In one embodiment, the UVB exposure data may containthe captured UVB radiation amount and intensity at the one or more UVradiation sensors, correlated with the data on the user's bodytemperature, date, time, latitude, and elevation of the user. At step704, the UVB exposure data may be loaded into the ML model module 452 b.

In one embodiment, in the scenario where the ML model was trained on thecombination of the user's UVB exposure data and the user's stress data,at step 702, in addition to receiving the one or more particular UVBexposure data sets, the ML model module 452 b of server 450 may alsoreceive one or more particular stress data sets from one or more datasources. In some embodiments, this data may be collected from one ormore smart ring sensors 105, the user device 422 (e.g., a smart phone),or the mobile device 444 (e.g., a stress tracking device). In oneembodiment, the stress data may contain the user's physiological dataacquired from the one or more physiological sensors, the data on theuser's level of stress hormone or hormones acquired from the one or moreelectrochemical sensors, the data on the user's gesticulation from theone or more motion sensors, the data on the user's hand grip pressureacquired from the one or more pressure sensors, and audio data from theone or more microphones. These data, for example, may containmeasurements of the user's heart rate, blood pressure, body temperature,electrodermal activity, sweat amount and sweat concentration ofparticular stress hormones, blood levels of particular substances, handmovements and gestures indicative of a person under stress, the data onsounds and utterances from the user indicative of a person under stress,and a date and time stamp of these measurements. In one embodiment, atstep 704, the UVB exposure and the stress data may be loaded into the MLmodel module 452 b.

In an embodiment where the ML model first training data set included UVBexposure patterns for users other than the user associated with thesmart ring, at the step 702, the ML model module 452 b of server 450 mayreceive particular UVB exposure data sets that may include data on theindividuals' skin tone, age, and weight, from one or more data sourcesdescribed above (the ring 405, or the user device 422, or the mobiledevice 444).

At step 706 ML model module 452 b may determine that particular UVBexposure data correlates to a particular level of driving risk exposure,which is determined at step 606 of the ML model. For example, the module452 b may determine that a particular combination of the detected amountof UVB exposure (determined from a combination of user UVB radiationexposure amount, body temperature, and the estimated clothing type)correlates with high risk driving behavior (e.g., faster driving, highacceleration, more aggressive turning or braking, more accidents, closeraverage proximity to other vehicles or pedestrians, etc.). Likewise,other factors represented by the stress data (e.g., particular hand grippressure(s), gesticulation(s), hear rate(s), blood pressure(s), bodytemperature(s), skin conductance(s), sweat amount(s) or sweatcomposition(s), spoken words(s) or sound(s), etc.) may correlate withhigh risk driving behavior.

At step 708, a communication unit of an output device of system 100 (oneor more implementations of the ring output unit 190, or one or more ofthe display technologies depicted in FIG. 5 ) may alert the smart ringuser of the predicted level of driving risk exposure (e.g., representedby a driving risk score). For example, the alert may be visual (e.g., awritten text, an image, a color code, etc.), haptic, thermal, or anaudio alert. Step 708 may or may not be implemented, depending on theembodiment.

In various embodiments, an analyzing device (whether it is the server450, or the ring 405, or the user device 422, or the mobile device 444),may use the particular UVB exposure data (or, as in one embodiment, thecombination of the particular UVB exposure data and the particularstress data) and the assessed level of driving risk exposure to make adetermination of a suggested action or actions to improve the particulardriving risk (step 710).

In some embodiments, the analyzing device, at step 712, may assesswhether the calculated driving risk exceeds a pre-determined thresholdindicating that the ring user's condition is not fit for safe driving.If evaluation at step 712 yields that the driving risk score does notexceed the threshold but presents a probability of high-risk drivingbehavior, then further assessment at step 714 determines a suitable useraction to reduce or eliminate the current driving risk. For instance,the suggested user action may be to spend a certain amount of timeoutside in daylight to increase the skin UVB exposure, or take a vitaminD supplement, or, if the safe recommended levels of UVB exposure havebeen reached for the day, reduce UVB exposure by properly covering up,applying sunscreen, staying indoors, or rolling up the windows in thevehicle, if operating a vehicle.

At step 718, similarly to step 708, a communication unit of an outputdevice may relay to the smart ring user the suggested user action. Inthe case of a positive determination at step 712, the analyzing devicefurther determines a system action (step 716), which can include one ora combination of actions to block or overtake the user's vehicle controlelements 802, such as ignition 804, brakes 806, or other 808 (see FIG. 8), and at step 720 performs the system action by communicating it tovehicle controller 812. We must note that the described paths are notmutually exclusive, and that each of the steps 718 and 720 may or maynot be implemented, depending on the embodiment. For example, animplementation may select to communicate user action only, orcommunicate user action and system action, or communicate system actionand perform system action, or perform system action only, etc.

In some embodiments, in addition to determining the driving risk scorebased at least in part upon the user's UVB exposure indicators prior toa driving session, the analyzing device may add to its analysis dataindicative of the driver's UVB exposure (or, as in one embodiment, acombination of UVB exposure and stress data) in real time during adriving session. As an example, the smart ring or other capable devicesmay collect data on the user's UVB exposure, and physiological andbehavioral parameters. The same or a different machine learning modelwould correlate this additional data with the saved driving data, and/ordriving data of that session, and adjust the level of driving riskexposure in real time. The model may also correlate the real-time dataon the indicated user parameters with driver compliance with thesuggested remediating action. The analyzing device may then determine anew remediating user or system action. In the case of the latter, thesystem may interfere or overtake vehicle operation, thus preventing theuser from further driving.

For instance, the smart ring system might assess the ring user's drivingfitness prior to a driving session and determine a driving risk scoreclose to a threshold score. The system may suggest to the user to spenda certain amount of time outside in daylight or carry out any othersuitable strategy to remediate the driving risk exposure. The drivermight ignore this suggestion and initiate a driving session. After aperiod of time, for example, the driver's UVB exposure levels mightremain unchanged, and the stress levels might increase. The ML model mayprocess the driver's new parameters, as well as real time driving data,and determine a driving risk score at or above a threshold score. Inthis scenario, the driver may be prevented from further driving byeither stopping and parking the vehicle in a safe location or switchingthe vehicle into autonomous mode.

In some embodiments, any of the suggested communication systems maycommunicate the acquired UVB exposure data, the determined driving riskscore, the suggested remediation, and whether any actions were taken bythe user, to the user's insurance provider (e.g., vehicle or healthinsurance provider). Such data can be used for real-time insuranceadjustment, in a gamified environment of extrinsic rewards andmotivators, or used in conjunction with other means of enforcingcompliance with suggested remediating actions.

VIII. Example of Vehicle Control Elements and Vehicle Monitor Components

FIG. 8 shows elements of the vehicle 446 or 108, which may be incommunication with the smart ring 101 or 405 and its components.Specifically, at a high level the vehicle 446 may include a set ofvehicle control elements 802, which are controlled to operate thevehicle 446. The vehicle 446 may include the vehicle computer 810, whichis a built-in computer system for the vehicle 446. The vehicle computer810 may control a display (not shown) integrated into the dash orconsole of the vehicle 446 (e.g., to display speed, RPM,miles-per-gallon, a navigation interface, an entertainment interface,etc.) and may be referred to as a built-in vehicle computer, a carputer,an integrated vehicle computer, etc.

Vehicle control elements 802 may be in communication with other smartring system (e.g., via vehicle controller 812), components tocommunicate or implement a remediating action in accordance with variousembodiments disclosed therein.

The vehicle control elements may include ignition 804, brakes 806, andother components 808. As discussed below, the controller 812 maycommunicate with any one of the components 804, 806, 808 to preventvehicle operation or overtake vehicle operation and resume it inautonomous mode as part of a remediation action after predicting a highdriver risk exposure level or risk score. In an embodiment, vehiclesensors 814 may provide driving training data for the ML model trainingmodule 452 a.

The vehicle computer 810 may include a controller 812 and sensors 814.While not shown, the controller 812 may include a memory and aprocessor, and the vehicle computer 810 may include a communicationinterface. The controller 812 may communicate with the vehicle controlelements 802, implementing system actions of step 716. The controller812 may also coordinate data generation and output from the sensors 814.The sensors 814 may be configured to collect data to enable tracking ofthe behavior of the vehicle 446 (e.g., braking,accelerating/decelerating, swerving, proximity to other vehicles,adherence to lane markers and other road markers, adherence to speedlimits, etc.). The sensors 814 may include a speedometer; one or moreaccelerometers; one or more cameras, image sensors, laser sensors, RADARsensors, or infrared sensors directed to the road surface, to potentialobstacles on the road, or to the driver (e.g., for autonomous orsemi-autonomous driving); a dedicated GPS receiver (not shown) disposedin the vehicle (e.g., in the interior, such as in the cabin, trunk, orengine compartment, or on the exterior of the vehicle); a compass; etc.

IX. Examples of Additional Functionality

In one embodiment, additionally or alternatively to predicting the levelof driving risk exposure, the described system and method may estimatethe amount of vitamin D produced in the user's skin, and furthercommunicate a suitable recommendation based at least in part upon theobtained result, or inform the user that the recommended level of dailyvitamin D has been reached. FIG. 9 illustrates a flow diagram of anexemplary method 900 describing this functionality.

The method 900 may be implemented by way of all, or part, or thecomputing devices, features, and/or other functionality describedregarding FIG. 1 , FIG. 4 , and FIG. 5 . At a high level, an analyzingdevice (whether it is the server 450, or the ring 405, or the userdevice 422, or the mobile device 444) receives the necessary measuredand/or user-defined data enabling an algorithm to calculate the amountof vitamin D generated in the user's skin based at least in part uponthe provided parameters. In an embodiment, the determined level ofvitamin D may be a ternary parameter having three possible values (e.g.,high, normal, and low) or it may be any suitable score metric thatcorresponds to a range of recommended values of vitamin D blood content,or the recommended daily vitamin D intake. The analyzing device furthermakes a determination based at least in part upon whether the determinedamount was below, at or above the recommended daily level. Then, basedat least in part upon the said determination, a recommendation may becommunicated to the user (e.g., via one of the example display devices500 of the system 100, or other suitable devices of the ring output unit190).

More specifically, at step 902, the analyzing device receives data onUVB radiation intensity and exposure time from the one or more lightintensity meters associated with the user. This data may be acquired inunits of Joules per square area of exposed surface. In some embodiments,the data may be collected from any suitable device outfitted with alight intensity meter with a sensitivity in the 280-315 nm region, forexample the smart ring 405 (equipped with the one or more ring sensors150), the user device 444 (e.g., a dedicated user UVB exposuremonitoring device), or the mobile device 422 equipped with the abilityto collect and transmit a variety of data indicative of user UVBexposure patterns (e.g., a smart phone).

At step 904, the analyzing device receives data on the user's bodytemperature correlated with the measurements on UVB radiation intensityand exposure times. In some embodiments, the data may be collected fromany suitable device, for example the smart ring 405 (equipped with theone or more ring sensors 150), or any other device (e.g., device 444, or442) capable of capturing the user's body temperature in real time.

At step 910, the amount of vitamin D generated in the user's skin may becalculated from any one or more parameters obtained at steps orsub-steps 902, 904, 920, and 919. This calculation may be performed at acertain time daily, at certain intervals (for example, hourly), or inreal time. The data received from the user input 919 may contain any oneor more parameters from a stored user input 920 on user skin tone 922,user age 924, and user weight 926. User input may be collected from anyone of the suitable devices, for example the user input unit 170 of thesmart ring 405 (e.g., a projected keyboard), the user device 444 (e.g.,a laptop), the mobile device 422 (e.g., a mobile phone), or the vehicle446 (e.g., an interactive vehicle dashboard). In an embodiment, the userskin tone 922 may also be obtained from the smart ring 405 equipped withthe one or more ring sensors 150 (e.g., a spectrophotometer). User inputdata 919 may include a daily user input 930, containing an input ofdaily clothing type 932 (e.g., a selection from provided images ofclothing styles, their descriptions, and a selection of fabric type andthickness). The user clothing type and coverage may also be estimatedfrom the date, ambient temperature, and/or latitude and elevation dataobtained from one of the described suitable sensors or any othersuitable means.

At step 940, the amount of vitamin D generated in the user's skin,calculated at step 910 (e.g., measured in a variable that corresponds tomicrograms or International Units (IU)) may be compared to therecommended daily vitamin D levels, for example the recommended dailyintake levels (400-800 IU, or 10-20 micrograms).

In some embodiments, the analyzing device, at step 942, may assesswhether the calculated amount of vitamin D generated in the user's skinis below, at, or above the daily recommended levels. Such an evaluationmay be made, for example, hourly, or in shorter increments of time(e.g., every ten minutes). If the evaluation at step 942 yields that theamount of vitamin D calculated at step 910 is below the recommendedlevel, then further assessment at step 944 determines a suitable useraction to reduce or eliminate the current driving risk. For instance,the suggested user action may be to spend a certain amount of timeoutside in daylight to increase the skin UVB exposure or take a vitaminD supplement. The said determination at step 944 may be communicated,for example, hourly, or at a set time daily. The frequency of step 944may also depend on the rate of vitamin D accumulation. If the evaluationat step 942 yields that the amount of vitamin D calculated at step 910is not below the recommended level, then the user may be informed thatthe recommended level of daily vitamin D has been reached. Such an alertmay be generated as soon as the determination has been made.

At steps 944 and 946, the smart ring user may be alerted by acommunication unit of an output device of system 100 (one or moreimplementations of the ring output unit 190, or one or more of thedisplay technologies depicted in FIG. 5 ). For example, the alert may bevisual (e.g., a written text, an image, a color code, etc.), haptic,thermal, or an audio alert.

X. Examples of Other Considerations

When implemented in software, any of the applications, services, andengines described herein may be stored in any tangible, non-transitorycomputer readable memory such as on a magnetic disk, a laser disk, solidstate memory device, molecular memory storage device, or other storagemedium, in a RAM or ROM of a computer or processor, etc. Although theexample systems disclosed herein are disclosed as including, among othercomponents, software or firmware executed on hardware, it should benoted that such systems are merely illustrative and should not beconsidered as limiting. For example, it is contemplated that any or allof these hardware, software, and firmware components could be embodiedexclusively in hardware, exclusively in software, or in any combinationof hardware and software. Accordingly, while the example systemsdescribed herein are described as being implemented in software executedon a processor of one or more computer devices, persons of ordinaryskill in the art will readily appreciate that the examples provided arenot the only way to implement such systems.

The described functions may be implemented, in whole or in part, by thedevices, circuits, or routines of the system 100 shown in FIG. 1 . Eachof the described methods may be embodied by a set of circuits that arepermanently or semi-permanently configured (e.g., an ASIC or FPGA) toperform logical functions of the respective method or that are at leasttemporarily configured (e.g., one or more processors and a setinstructions or routines, representing the logical functions, saved to amemory) to perform the logical functions of the respective method.

XI. Examples of General Terms and Phrases

Throughout this specification, some of the following terms and phrasesare used.

Bus, according to some embodiments: Generally speaking, a bus is acommunication system that transfers information between componentsinside a computer system, or between computer systems. A processor or aparticular system (e.g., the processor 454 of the server 450) orsubsystem may communicate with other components of the system orsubsystem (e.g., the components 452 and 456) via one or morecommunication links. When communicating with components in a sharedhousing, for example, the processor may be communicatively connected tocomponents by a system bus. Unless stated otherwise, as used herein thephrase “system bus” and the term “bus” refer to: a data bus (forcarrying data), an address bus (for determining where the data should besent), a control bus (for determining the operation to execute), or somecombination thereof. Depending on the context, “system bus” or “bus” mayrefer to any of several types of bus structures including a memory busor memory controller, a peripheral bus, or a local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus also known as Mezzanine bus.

Communication Interface, according to some embodiments: Some of thedescribed devices or systems include a “communication interface”(sometimes referred to as a “network interface”). A communicationinterface enables the system to send information to other systems and toreceive information from other systems and may include circuitry forwired or wireless communication.

Each described communication interface or communications unit (e.g.,communications unit 160) may enable the device of which it is a part toconnect to components or to other computing systems or servers via anysuitable network, such as a personal area network (PAN), a local areanetwork (LAN), or a wide area network (WAN). In particular, thecommunication unit 160 may include circuitry for wirelessly connectingthe smart ring 101 to the user device 104 or the network 105 inaccordance with protocols and standards for NFC (operating in the 13.56MHz band), RFID (operating in frequency bands of 125-134 kHz, 13.56 MHz,or 856 MHz to 960 MHz), Bluetooth (operating in a band of 2.4 to 2.485GHz), Wi-Fi Direct (operating in a band of 2.4 GHz or 5 GHz), or anyother suitable communications protocol or standard that enables wirelesscommunication.

Communication Link, according to some embodiments: A “communicationlink” or “link” is a pathway or medium connecting two or more nodes. Alink between two end-nodes may include one or more sublinks coupledtogether via one or more intermediary nodes. A link may be a physicallink or a logical link. A physical link is the interface or medium(s)over which information is transferred and may be wired or wireless innature. Examples of physicals links may include a cable with a conductorfor transmission of electrical energy, a fiber optic connection fortransmission of light, or a wireless electromagnetic signal that carriesinformation via changes made to one or more properties of anelectromagnetic wave(s).

A logical link between two or more nodes represents an abstraction ofthe underlying physical links or intermediary nodes connecting the twoor more nodes. For example, two or more nodes may be logically coupledvia a logical link. The logical link may be established via anycombination of physical links and intermediary nodes (e.g., routers,switches, or other networking equipment).

A link is sometimes referred to as a “communication channel.” In awireless communication system, the term “communication channel” (or just“channel”) generally refers to a particular frequency or frequency band.A carrier signal (or carrier wave) may be transmitted at the particularfrequency or within the particular frequency band of the channel. Insome instances, multiple signals may be transmitted over a singleband/channel. For example, signals may sometimes be simultaneouslytransmitted over a single band/channel via different sub-bands orsub-channels. As another example, signals may sometimes be transmittedvia the same band by allocating time slots over which respectivetransmitters and receivers use the band in question.

Machine Learning, according to some embodiments: Generally speaking,machine-learning is a method of data analysis that automates analyticalmodel building. Specifically, machine-learning generally refers to thealgorithms and models that computer systems use to effectively perform aspecific task without using explicit instructions, relying on patternsand inference instead. Machine-learning algorithms learn through aprocess called induction or inductive learning. Induction is a reasoningprocess that makes generalizations (a model) from specific information(training data).

Generalization is needed because the model that is prepared by amachine-learning algorithm needs to make predictions or decisions basedat least in part upon specific data instances that were not seen duringtraining. Note, a model may suffer from over-learning or under-learning.

Over-learning occurs when a model learns the training data too closelyand does not generalize. The result is poor performance on data otherthan the training dataset. This is also called over-fitting.

Under-learning occurs when a model has not learned enough structure fromthe training data because the learning process was terminated early. Theresult is good generalization but poor performance on all data,including the training dataset. This is also called under-fitting.

Memory and Computer-Readable Media, according to some embodiments:Generally speaking, as used herein the phrase “memory” or “memorydevice” refers to a system or device (e.g., the memory unit 144)including computer-readable media (“CRM”). “CRM” refers to a medium ormedia accessible by the relevant computing system for placing, keeping,or retrieving information (e.g., data, computer-readable instructions,program modules, applications, routines, etc.). Note, “CRM” refers tomedia that is non-transitory in nature, and does not refer todisembodied transitory signals, such as radio waves.

The CRM may be implemented in any technology, device, or group ofdevices included in the relevant computing system or in communicationwith the relevant computing system. The CRM may include volatile ornonvolatile media, and removable or non-removable media. The CRM mayinclude, but is not limited to, RAM, ROM, EEPROM, flash memory, or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store information, and which can be accessed by the computingsystem. The CRM may be communicatively coupled to a system bus, enablingcommunication between the CRM and other systems or components coupled tothe system bus. In some implementations the CRM may be coupled to thesystem bus via a memory interface (e.g., a memory controller). A memoryinterface is circuitry that manages the flow of data between the CRM andthe system bus.

Network, according to some embodiments: As used herein and unlessotherwise specified, when used in the context of system(s) or device(s)that communicate information or data, the term “network” (e.g., thenetworks 105 and 440) refers to a collection of nodes (e.g., devices orsystems capable of sending, receiving or forwarding information) andlinks which are connected to enable telecommunication between the nodes.

Each of the described networks may include dedicated routers responsiblefor directing traffic between nodes, and, optionally, dedicated devicesresponsible for configuring and managing the network. Some or all of thenodes may be also adapted to function as routers in order to directtraffic sent between other network devices. Network devices may beinter-connected in a wired or wireless manner, and network devices mayhave different routing and transfer capabilities. For example, dedicatedrouters may be capable of high-volume transmissions while some nodes maybe capable of sending and receiving relatively little traffic over thesame period of time. Additionally, the connections between nodes on anetwork may have different throughput capabilities and differentattenuation characteristics. A fiberoptic cable, for example, may becapable of providing a bandwidth several orders of magnitude higher thana wireless link because of the difference in the inherent physicallimitations of the medium. If desired, each described network mayinclude networks or sub-networks, such as a local area network (LAN) ora wide area network (WAN).

Node, according to some embodiments: Generally speaking, the term “node”refers to a connection point, redistribution point, or a communicationendpoint. A node may be any device or system (e.g., a computer system)capable of sending, receiving or forwarding information. For example,end-devices or end-systems that originate or ultimately receive amessage are nodes. Intermediary devices that receive and forward themessage (e.g., between two end-devices) are also generally considered tobe “nodes.”

Processor, according to some embodiments: The various operations ofexample methods described herein may be performed, at least partially,by one or more processors (e.g., the one or more processors in theprocessor unit 142). Generally speaking, the terms “processor” and“microprocessor” are used interchangeably, each referring to a computerprocessor configured to fetch and execute instructions stored to memory.By executing these instructions, the processor(s) can carry out variousoperations or functions defined by the instructions. The processor(s)may be temporarily configured (e.g., by instructions or software) orpermanently configured to perform the relevant operations or functions(e.g., a processor for an Application Specific Integrated Circuit, orASIC), depending on the particular embodiment. A processor may be partof a chipset, which may also include, for example, a memory controlleror an I/O controller. A chipset is a collection of electronic componentsin an integrated circuit that is typically configured to provide I/O andmemory management functions as well as a plurality of general purpose orspecial purpose registers, timers, etc. Generally speaking, one or moreof the described processors may be communicatively coupled to othercomponents (such as memory devices and I/O devices) via a system bus.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the processor or processors may be located in a single location (e.g.,within a home environment, an office environment or as a server farm),while in other embodiments the processors may be distributed across anumber of locations.

Words such as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

Although specific embodiments of the present disclosure have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the present disclosure is notto be limited by the specific illustrated embodiments.

1-20. (canceled)
 21. A method for predicting driving risk exposure basedat least in part upon observed light exposure patterns, the methodcomprising: receiving one or more sets of first data indicative of oneor more light exposure patterns acquired via one or more light exposuremonitoring devices; receiving one or more sets of second data indicativeof one or more driving patterns acquired via one or more driving monitordevices disposed on or within a vehicle; utilizing the one or more setsof first data and the one or more sets of second data as training datafor a machine learning (ML) model to train the ML model to identify oneor more relationships between the one or more light exposure patternsand the one or more driving patterns, wherein the one or morerelationships include a relationship representing a correlation betweena given light exposure pattern and a high-risk driving pattern;receiving a particular set of data acquired via a particular smart ringassociated with a user; analyzing, via the ML model, the particular setof data collected by the particular smart ring associated with the user,wherein the analyzing includes: determining that the particular set ofdata represents a particular light exposure pattern corresponding to thegiven light exposure pattern correlated with the high-risk drivingpattern; and predicting, based at least in part upon the ML model, alevel of risk exposure for the user during driving; and generating anotification to alert the user of the predicted level of risk exposure.22. The method of claim 21, wherein the one or more sets of first datacomprises radiation data acquired via one or more light sensors.
 23. Themethod of claim 21, wherein the one or more sets of first data comprisesdate and latitude data acquired via one or more GPS sensors and bodytemperature data acquired via one or more temperature sensors.
 24. Themethod of claim 21, wherein the one or more sets of first data comprisesskin tone data acquired via one or more spectrophotometers.
 25. Themethod of claim 21, wherein the one or more sets of first data comprisesskin tone, age, weight, or clothing type.
 26. The method of claim 21,wherein the one or more driving monitor devices include a vehiclecomputer or a dedicated electronic driving tracker device.
 27. Themethod of claim 21, further comprising: providing the generatednotification to the particular smart ring or a display of the vehicle.28. The method of claim 21, further comprising: comparing the predictedlevel of risk exposure to a known threshold to determine whether thepredicted level of risk exposure exceeds the known threshold; inresponse to the predicted level of risk exposure exceeding the knownthreshold, generating a system action preventing the user from operatingthe vehicle, wherein the preventing includes preventing the user fromstarting the vehicle or overtaking control of the vehicle while thevehicle is in operation.
 29. The method of claim 21, further comprising:utilizing the particular set of data and the particular light exposurepattern to further train the ML model.
 30. A system for acquiring dataindicative of light exposure patterns, and utilizing the data to predictdriving risk exposure, comprising: a server configured to: receive oneor more sets of first data indicative of one or more light exposurepatterns collected by one or more light exposure monitoring devices;receive one or more sets of second data indicative of one or moredriving patterns collected by one or more driving monitor devicesdisposed within a vehicle; utilize the one or more sets of first dataand the one or more sets of second data as training data for a machinelearning (ML) model to train the ML model to discover one or morerelationships between the one or more light exposure patterns and theone or more driving patterns, wherein the one or more relationshipsinclude a relationship representing a correlation between a given lightexposure pattern and a high-risk driving pattern; receive a particularset of data acquired via a smart ring associated with a user; analyze,via the ML model, the particular set of data collected by the particularsmart ring; determine that the particular set of data represents aparticular light exposure pattern corresponding to the given lightexposure pattern correlated with the high-risk driving pattern; predict,via the ML model, a level of risk exposure for the user during driving;and generate a notification to alert the user of the predicted level ofrisk exposure.
 31. The system of claim 30, wherein the one or more setsof first data comprises radiation data acquired via one or more lightsensors.
 32. The system of claim 30, wherein the one or more sets offirst data comprises date and latitude data acquired via one or more GPSsensors and body temperature data acquired via one or more temperaturesensors.
 33. The system of claim 30, wherein the smart ring has an innerdiameter within a range between 13 mm and 23 mm.
 34. The system of claim30, wherein the server is configured to generate the notification toalert the user of the predicted level of risk exposure by way ofgenerating the notification via the smart ring, a vehicle computer, or amobile device in communication with the server.
 35. The system of claim30, wherein the one or more sets of first data includes light exposurepattern data for users other than the user associated with the smartring.
 36. The system of claim 30, wherein the one or more sets of seconddata includes driving pattern data for users other than the userassociated with the smart ring.
 37. A non-transitory computer-readablemedium storing instructions for implementing a machine learning model topredict driving risk exposure based at least in part upon acquired lightexposure patterns, wherein the instructions, when executed by one ormore processors, cause the one or more processors to: receive one ormore sets of first data indicative of one or more light exposurepatterns; receive one or more sets of second data indicative of one ormore driving patterns; utilize the one or more sets of first data andthe one or more sets of second data as training data for a machinelearning (ML) model to train the ML model to discover one or morerelationships between the one or more light exposure patterns and theone or more driving patterns, wherein the one or more relationshipsinclude a relationship representing a correlation between a given lightexposure pattern and a high-risk driving pattern; analyze, via the MLmodel, a particular set of data collected by a smart ring by:determining that the particular set of data represents a particularlight exposure pattern corresponding to the given light exposure patterncorrelated with the high-risk driving pattern; and predicting, based atleast in part upon the ML model, a level of risk exposure for the userduring driving; and generate a notification to alert the user of thepredicted level of risk exposure.
 38. The non-transitorycomputer-readable medium of claim 37, wherein the instructions furthercause the one or more processors to transmit the notification to thesmart ring, a vehicle computer, or a mobile device.
 39. Thenon-transitory computer-readable medium of claim 37, wherein thepredicted level of risk exposure is a binary or ternary parameter. 40.The non-transitory computer-readable medium of claim 37, wherein theinstructions further cause the one or more processors to transmit thenotification to: compare the predicted level of risk exposure to athreshold; and if the predicted level of risk exposure exceeds thethreshold, generate a system action and transmit the system action to avehicle computer for a vehicle to cause the vehicle computer to preventthe user from operating the vehicle.